A Survey of Exact Inference for Contingency Tables Alan Agresti Statistical Science

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A Survey of Exact Inference for Contingency Tables
Alan Agresti
Statistical Science, Vol. 7, No. 1. (Feb., 1992), pp. 131-153.
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Statistical Science
1992,Vol. 7,No. 1,131-177
A Survey -of Exact Inference
for Contingency Tables
Alan Agresti
Abstract. The past decade has seen substantial research on exact inference for contingency tables, both in terms of developing new analyses
and developing efficient algorithms for computations. Coupled with
concomitant improvements in computer power, this research has resulted in a greater variety of exact procedures becoming feasible for
practical use and a considerable increase in the size of data sets to
which the procedures can be applied. For some basic analyses of contingency tables, it is unnecessary to use large-sample approximations to
sampling distributions when their adequacy is in doubt. This article
surveys the current theoretical and computational developments of
exact methods for contingency tables. Primary attention is given to the
exact conditional approach, which eliminates nuisance parameters by
conditioning on their sflicient statistics. The presentation of various
exact inferences is unified by expressing them in terms of parameters
and their sufficient statistics in loglinear models. Exact approaches for
many inferences are not yet addressed in the literature, particularly for
multidimensional contingency tables, and this article also suggests
additional research for the next decade that would make exact methods
yet more widely applicable.
Key words and phrases: Categorical data, chi-squared tests, computational algorithms, conditional inference, Fisher's exact test, logistic
regression, loglinear models, odds ratios, sufficient statistics.
Table of Contents 1. Introduction 1.1 Historical perspective 1.2 Outline and notation 1.3 The exact conditional approach 2. Exact Inference for 2 x 2 Tables 2.1 Fisher's exact test 2.2 "Exact" estimation in 2 x 2 tables 2.3 Comparing dependent proportions
'3. Exact Inference in I x J Tables'
3.1 Linking test statistics to alternatives
3.2 Exact estimation for I x J tables 4. Exact Inference in Three-Way Contingency Tables
4.1 Testing conditional independence in 2 x 2 x K tables 4.2 Testing homogeneity of odds ratios in 2 x 2 x K
tables f
Alan Agresti is Professor of Statistics, University of
Florida, Griffin-Floyd Hall, Gainesville, Florida
32611.
4.3 Exact estimation in 2 x 2 x K tables
4.4 Exact methods for I x J x K tables
5. Exact Inference for Logistic Regression Models
6. Exact Goodness of Fit
7. Computing Feasibility
7.1 Algorithms
7.2 Software
8. Other Approaches to Exact Inference
8.1 Controversy over exact conditional approaches
8.2 Exact unconditional approach
8.3 Bayesian approaches
9. Future Research
References
1. lNTRODUCTlON
This article surveys the development of exact
inferential methods for contingency tables. 1interrelate various inferences by expressing them in
terms of parameters in a hierarchy of loglinear
models. The presentation focuses primarily on
exact conditional methods, in which one obtains
sampling distributions not dependent on other
unknown parameters by conditioning on their
sufficient statistics. I also discuss some long-standing controversies for such methods and present
topics for additional research that should soon be
feasible given the continual improvement in computer hardware and software.
1.I Historical Perspective
Traditionally, statistical inference for contingency tables has relied heavily on large-sample
approximations for sampling distributions of parameter estimators and test statistics. Many of
these approximations are special cases of ones that
apply more generally than to categorical data [e.g.,
chi-squared approximations for likelihood-ratio
statistics and normal approximations for maximum
likelihood (ML) estimators of model parameters].
With this emphasis on large-sample methods, the
development of inferential methods for categorical
data parallels the historically earlier development
of inferential methods for continuous data. For instance, E. S. Pearson's recently published manuscript about Student notes that studies analyzed by
Karl Pearson's laboratory usually involved largesize data sets. When Gosset presented his queries
about small samples that led to his development of
techniques using the t-distribution, Pearson replied,
"Only naughty brewers deal in small samples"
(Pearson, 1990, page 73).
R. A. Fisher's Statistical Methods for Research
Workers was at the forefront of advocating exact
procedures for small samples. In the preface of the
first edition of that book (1925), Fisher stated,
" . . . the traditional machinery of statistical processes is wholly unsuited to the needs of practical
research. Not only does it take a cannon to shoot a
sparrow, but it misses the sparrow! The elaborate
mechanism built on the theory of infinitely large
samples is not accurate enough for simple laboratory data. Only by systematically tackling small
sample problems on their merits does it seem possible to apply accurate tests to practical data." Not
surprisingly, t-procedures received strong emphasis
iq that text, and "Fisher's exact ,test" for 2 x 2
contingency tables appeared in the 1934 and
subsequent editions.
The importance of improving the scope of exact
methods for categorical data has become increasingly clear in recent years. Standard asymptotic
methods apply to a fixed number of cells, as cell
expected frequencies grow to infinity. Yet, researchers often attempt to alialyze additional variables as the sample size grows; thus, large expected
frequencies may be the exception rather than the
norm. Although recent research has introduced new
asymptotic approaches that permit the number of
cells to grow as the sample size grows (e.g., Morris,
1975; Haberman, 1977; Koehler, 1986; McCullagh,
1986; Zelterman, 1987), information on the adequacy of these approximations for standard models
is at an infant stage. [Cressie and Read (1989)
surveyed research on the adequacy of various
asymptotic approximations.] Also, simulation studies have shown that it is hopeless to expect simple
guidelines to indicate when asymptotic large-sample approximations are adequate (e.g., Koehler and
Larntz, 1980). Even when the sample size is quite
large, recent work has shown that large-sample
approximations can be very poor when the contingency table contains both small and large expected
frequencies (Haberman, 1988). Regarding adequacy of asymptotics, the sample size n often has
less relevance than the discreteness of the sampling distribution. Thus, "small-sample methods"
for categorical data more accurately refer to
methods needed when there are few points or relatively large probabilities in the support of that
distribution.
Table 1, taken from Graubard and Korn (1987),
illustrates that different statistics and approximations can give quite different results, even for very
large samples. That table, which refers to a
prospective study of maternal drinking and congenital malformations, has 32,574 observations. For
testing independence of alcohol consumption and
malformation, asymptotic chi-squared tests give pvalues of 0.017 using the Pearson statistic and
0.190 using the likelihood-ratio statistic. Exact tests
of a type discussed in Section 3.1 using these criteria give p-values of 0.034 and 0.139, respectively.
A test based on a trend alternative that utilizes the
ordering of column categories by assigning scores
(0,0.5,1.5,4,7) to them has exact p-value more
than three times the p-value based on an asymptotic normal approximation (0.017 versus 0.005 in
the one-sided case).
The lag in the development and use of exact
inferences for contingency tables is partly explained by the later development of methods for
categorical data compared with continuous data,
but also by the greater computational complexity.
However, recent advances in computational power
TABLE1 Maternal drinking and congenital malformations Alcohol consumption
(average no. of drinks/day)
Malformation
0
<1
1-2
3-5
2
Absent
Present
17,066
48
14,464
38
788
5
126
1
37 Source: Graubard and Korn (1987).
6
1 EXACT INFERENCE FOR CONTINGENCY TABLES
and efficiency of algorithms have made exact methods feasible for a wider variety of inferential analyses and for a larger collection of table sizes and
sample sizes. In this survey of exact inferences for
contingency tables, I indicate which computations
can currently be done and also highlight areas in
which current capabilities are inadequate.
For two-way contingency tables, let X denote the row classification and Y the column classification. For three-way tables, denote the third classification by Z. For simplicity, denote loglinear models by standard symbols pertaining to their minimal sufficient statistics. For instance, (X, Y) denotes the model of statistical independence in a two-way table, and (XZ, YZ) denotes the model of conditional independence between X and Y, given Z, in a three-way table. The minimal sufficient statistics are i n i + ) and {n+j) for ( X , Y) and in,,,) and {n+jk) for (XZ, YZ). Finally, denote expected frequencies by m and sample proportions by p, for example, {mij = E(nij)} and { pij = nij/n} in a two-way table. 1.2 Outline and Notation
Sections 2 to 4 present a variety of exact methods
for contingency tables in the context of loglinear
models. Sections 2 and 3 focus on two-way tables,
Section 2 for the 2 x 2 case and Section 3 for the
I x J case. Section 4 focuses on three-way tables,
with emphasis on the 2 x 2 x K case. Section 5
discusses exact inference for logistic regression
models, and Section 6 discusses exact goodness-of-fit
testing for loglinear and logistic models. Section 7
discusses computing feasibility of exact methods,
using currently available software.
Nearly all the literature on exact methods for
contingency tables emphasizes hypothesis testing;
but, in each section, I also indicate the scope
of work on interval estimation. My discussion
throughout the article takes the viewpoint of classical frequentist conditional inference, with application to loglinear models for contingency tables.
Section 8 mentions other approaches. It presents an
unconditional approach to exact inference and also
indicates how Bayesian inferences correspond to
conditional inferences for certain choices of prior
distributions. The final section discusses possible
future directions for research on exact methods for
contingency tables. -.
Throughout the article, I assume a standard Poisson or mu1tinorr)ial sampling model for cell counts
in the contingency table. For instance, in an I x J
table, the cell counts inij) might have a multinomial distribution generated by n independent trials with IJ cell probabilities {.rrij). Or, the counts
inij, j = 1 , . . . , J ) in row i might have a multinomial distribution, with counts in different rows
,being independent. In the first case ("full" multinomial sampling), n = CC nij is fixed. In the second case (independent multinomial sampling),
i n i + = Cjnij, i = 1 , . . . , I ) are fixed. When inij)
are independent Poisson random variables, conditioning on n yields full multinomial sampling;
conditioning further on the row totals yields independent multinomial sampling. All sampling models lead to the same exact inferences, since those
inferences condition on marginal totals that contain as a subset the naturally fixed totals, and
since the parameters of usual interest are not the
proportions in the margins that are fixed under
some sampling designs but not under others.
133
1.3 The Exact Conditional Approach
Historically, the most common approach to exact inference in contingency tables has been a conditional one. Suppose an inference refers to a parameter in some loglinear model. Exact conditional inferential methods utilize the distribution of the sufficient statistic for that parameter, conditional on sufficient statistics for the other parameters ("nuisance" parameters) in the model. For in-
stance, suppose one wants to test a hypothesis Ho that corresponds to a loglinear model symbolized by Mo, under the assumption that a more general model M, holds, corresponding to an alternative hypothesis HI. Denote minimal sufficient statistics for the models by To and TI. Exact inference uses the conditional distribution of T, given To (e.g., Andersen, 1974). By the definition of sufficiency, the conditional distribution does not depend on the nuisance parameters, thus making exact inference possible. To illustrate, for Poisson sampling in a 2 x 2 contingency table, the saturated loglinear model has the form '
The parameters {Aij) describing association in this
model pertain to the odds ratio. For instance, suppose we achieve identifiability by setting
= %=
&, = A,, = &, = 0. Then A,, =. log(8), where 8 deThe
~ ~ m ~ ~
notes the odds ratio, 8 = ( m ~ ~ m ~ ~ ) / ( m
parameter set is (p, A?, A,; A,,), and normally interest focuses on 8 = exp(A,,), the others being nuisance parameters. The sufficient statistics are n for ,; and n,, for A,,. To conduct p, n,, for A?, n,, for A
exact inference about 8, consider the conditional distribution of n,, given n, n,,, and n,,. This conditional distribution is the same as the one having elements P(Y, = n,,, Y, = n,, - n,, I Y,
Y, = n+,), where {Yi, i = 1 , 2 ) are indepen-
+
134
A. AGRESTI
dent binomial random variables with parameters
mi,)]. Straightforward calculation shows that it equals
[ ni+, mi, /(mi,
+
the model by conditioning on sufficient statistics
for them.
2. EXACT INFERENCE FOR 2 x 2 TABLES
where the index of summation ranges from
max(0, n,++ n+, - n) to min(n,+, n+,), the possible values for n,, for the given marginal totals.
This is the noncentral hypergeometric distribution
(Fisher, 1935a; Cornfield, 1956).
To test statistical independence of X and Y [A,,
= 0 in model (1.1)1, one uses this distribution with
8 = 1. Of course, to complete a test, one needs to
specify a test statistic and the way to compute the
p-value. In testing a loglinear model M, against a
more complex model MI, in this article I will generally base p-values on the exact conditional distribution of Rao's efficient score statistic for testing
that the extra parameters that are in M, but not
in Mo equal zero. The efficient score statistic is
based on the vector of partial derivatives of the log
likelihood with respect to the extra parameters,
evaluated at the null estimates (Rao, 1973, Section
6e). For testing independence in a 2 x 2 table, this
suggests [ n,, - (n,+n+,)/ nl, suitably normalized,
as a test statistic.
The p-value, based on extreme values of the test
statistic, is calculated using the distribution for
that statistic that is_induced by the exact conditional distribution of { nij) . When M, contains one
more parameter than M,, tests based on the efficient score statistic are uniformly most powerful
unbiased (UMPU), as a consequence of a result for
exponential families (e.g., Lehmann, 1986, Theorem 3, page 147). Denote the ML estimator of the
parameters in M, by (e,, el), where 9, denotes the
estimator of the parameters that are in M, but not
in M,. For large samples with a fixed number of
cells, these tests are asymptotically equivalent to
~ a l tests
d
based on el and likelihood-ratio tests of
Mo against M, (Rao, 1973, pages 418-420).
My reason for relating exact conditional inference to loglinear models in this article is as
follows. Loglinear models are generalized linear
models for categorical data that use the canonical
link, that is, they directlyqmodel the natural parameter (the log mean) of a natural exponential
family (the Poisson). Such generalized linear models permit reduction of the data through sufficient
statistics (McCullagh and Nelder, 1989, page 32).
Thus, one can eliminate nuisance parameters in
This simple case still generates an enormous volume of publications, partly because of controversy
(discussed in Section 8.1) about applying exact
methods that condition on marginal totals that are
not naturally fixed under Poisson, multinomial, or
binomial sampling schemes. The most common
sampling model for 2 x 2 tables is independent
binomial sampling in the two rows. Denote the
"success" probabilities by a, and a,, for which the
odds ratio is 8 = [a, /(1 - a,)]/[a, /(1 - a,)]. The
hypothesis of homogeneity states that a, = a,. For
Poisson or full multinomial sampling, conditioning
on { n,+, n,+ ) gives binomial sampling, and statistical independence is equivalent to homogeneity.
Under the hypothesis of homogeneity, conditioning
as well on n+, to eliminate the nuisance parameter
(the common value of a, and a,) yields the hypergeometric distribution (1.2) with 8 = 1.
2.1 Fisher's Exact Test
Fisher's exact test (Fisher, 1934, 1935a; Yates,
1934; Irwin, 1935) of H,: 8 = 1 is "well known,"
and I refer the reader to Lehmann (1986, pages
151-162) for details of its derivation under various
sampling schemes. The null hypergeometric distribution has E(n,,) = n,+n+, / n and Var(n,,) =
n1+n+,n2+n+,/n2(n- 1). To test H,: 8 = 1
against HI: 8 > 1, the p-value is
where S = { t: t 2 n,,). The set S is identical to
that of tables for which the sample odds ratio is at
least as large as observed.
The hypergeometric applies directly as a sampling model when both sets of marginal counts are
naturally fixed. A classic example of this case is
Fisher's (1935b) tea-tasting experiment, relating to
a woman's claim to be able to judge whether tea or
milk was poured in the cup first. The woman was
given eight cups of tea, in four of which tea was
poured first and in four of which milk was poured
first, and was told to guess which four had tea
added first. The contingency table for this design
(see Table 2) has n = 8 and n,+= n+, = 4; n,, can
take values O,1,2,3,4 with corresponding one-sided
p-values (for HI: 8 > 1) of 1.0, 0.986, 0.757, 0.243
and 0.014.
Ways of forming two-sided p-values in Fisher's
exact test were discussed by Gibbons and Pratt
(1975), Yates and discussants (1984), Davis (1986),
EXACT INFERENCE FOR CONTINGENCY TABLES
TABLE2
Fisher's tea-tasting experiment
Guess poured first
Poured first
Milk
Tea
Total
Milk
Tea
3
1
1
3
4
4
Total
4
4
8
-
Dupont (1986), Mantel (1987), and Lloyd (1988a).
The most popular approaches are (a) double
the one-sided p-value, (b) (2.1) with S = { t:
f ( t l n, n,,, n,,) If(n1, I n, n,,, n,,)), and (c) (2.1)
with S = {t: I t - E(n,,)I 2 1 n,, - E(n,,)I}. The
third option is identical to the null probability that
the Pearson chi-squared statistic is at least as large
as observed. Different approaches can give different results because of the discreteness and potential skewness of the hypergeometric distribution.
For instance, consider the table having counts by
row (10,90/20,80) (i.e., 10 and 90 in the first row,
20 and 80 in the second), discussed by Dupont
(1986). The null distribution of n,, is symmetric
about 15, and the three p-values are identical and
equal 0.073. However, for the nearly identical table
(10,91/20,80), p-value (a) equals 0.069, whereas
(b) and (c) equal 0.050.
2.2 "Exact" Estimation in 2 x 2 Tables
The non-null conditional distribution (1.2) of n,,
is used in constructing "exact" confidence intervals
for the odds ratio. For data n,,, the conditional ML
estimate of 6 is t h e value of 19 that maximizes
probability (1.2). The estimate is obtained using
iterative methods (Cornfield, 1956), and differs from
the unconditional ML estimate 6 = n,, n,, / n,, n,,.
For instance, for Fisher's tea-tasting data (Table 2),
n,, = 3 and the unconditional ML estimate is 9; the
conditional ML estimate maximizes the conditional
likelihood (1.2),
and equals 6.41.
For testing H,: 8 = Oo against HI: 6 > O,, the
p-value is P = C,f(t I n, n,,, n+,;8,), where S =
{t: t 2 n,,}. For testing against H,: 0 < O, S = {t:
t I n,,}. One can obtain an "exact" confidence interval for 19 by inverting the test (Cornfield, 1956;
Mantel and Hankey, 1971'; Thomas, 1971). The
lower endpoint is the 8, value for which P = a / 2
in testing H,: 19 = 8, against H I : 8 > 8,. The upper
endpoint is the 8, value for which P = a / 2 in
testing Ho against H,: 8 < 8,. If n,, = 0, then the
lower endpoint is 0, and one uses P = a in obtain-
135
ing the upper endpoint; if n,, = min(n,+, n,,), then
the upper endpoint is m, and one uses P = a in
obtaining the lower endpoint. For the tea-tasting
data, the 95% confidence interval obtained in this
manner is (0.21,626.2).
The discreteness of the distribution of n,, limits
the confidence intervals to a discrete set of possible
endpoints, for fixed a . Thus, the true confidence
coefficient is at least 1 - a , rather than exactly
1 - a , and its value depends on the value of I9
(Neyman, 1935). It is strictly greater than 1 - a
unless the true 19 is an attainable endpoint. I use
quotes around "exact" in referring to such intervals to reflect this behavior.
Baptista and Pike (1977) described an alternative approach that also guarantees the confidence
level but sometimes gives slightly shorter intervals. Their interval is a generalization of Sterne's
(1954) confidence interval for a single binomial
parameter. One inverts a family of acceptance regions that are formed using the minimal number of
most likely outcomes. Specifically, for each 00, one
finds a set A(0,) of possible n,, values such that
the probability of the set is at least 1 - a and such
that every integer in the set is at least as likely to
occur as every integer outside the set. The confidence interval is the set of 8, values for which the
observed n,, falls in A(0,).
It is not possible to construct "exact" confidence
intervals for association measures that are not
functions of the odds ratio. They do not occur as
parameters in generalized linear models with Poisson or binomial random component using canonical
links. Thus, the usual conditioning arguments do
not eliminate nuisance parameters. For instance,
consider estimation of the difference of probabilities 6 = a, - a, for independent binomial samples.
The joint sampling distribution can be expressed in
terms of 6 and a,, for instance, but conditioning on
the marginal totals does not eliminate a,. Santner
and Snell (1980) discussed this and other difficulties in interval estimation of the difference of proportions and the relative risk. They also described
ways of getting conservative confidence intervals
for these parameters, but the usual conservativeness due to discreteness is compounded because of
the approach used to eliminate the nuisance
parameter.
"Exact" interval estimates exist in certain extreme situations in which asymptotic interval estimates do not. Suppose the conditional inference
uses the distribution of TI, given To. When T,
assumes its maximum or minimum value, the unconditional (or conditional) ML estimator of 6 and
its asymptotic standard error do not exist (unless
one adds some constant to the cells), but "exact"
136
A. AGRESTI
one-sided confidence intervals do exist. For a point
estimate in such cases, some authors (e.g., Hirji,
Mehta and Patel, 1987, 1988; Hirji, Tsiatis and
Mehta, 1989) use median unbiased estimates. To
illustrate, for a 2 x 2 table, when n,, = 0, the ML
estimator of the loglinear association parameter
(the log odds ratio) and the ML estimator ( C C l / nij)
of the asymptotic variance of the ML estimator do
not exist. The median unbiased estimate is the 8,
value that gives P = 0.5 and the "exact" upper
100(1 - a)% confidence limit is the 8, value that
gives P = a , in testing against HI: 8 < 8,.
2.3 Comparing Dependent Proportions
Next, consider the comparison of two proportions
when each sample contains the same subjects, or
the sample consists of matched pairs. For instance,
one might have repeated measurement of a binary
response at two occasions. Let nij be the number of
observations in category i at occasion 1 and category j at occasion 2. Let a i j denote the probability
of response i at occasion 1 and response j at occasion 2, and let pij = nij/n. The sample proportions
of " S U C C ~ S S ~ S "p l + at occasion 1 and p + , at occasion 2 are dependent, rather than independent,
because of the matching. The hypothesis of
marginal homogeneity, H,: a, = a ,, corresponds
to homogeneity for the 2 x 2 table consisting of the
two marginal distributions. For binary responses,
marginal homogeneity is equivalent to symmetry,
a,, = a,, To obtain an exact test, one conditions
on n* = n,, + n,,. Under H,, n,, has a binomial
(n*, 112) distribution. A two-sided p-value
is the sum of binomial probabilities for n,, values at least as far from n*/2 as observed. This
is a small-sample version of McNemar's test
(McNemar, 1947).
Cox (1958a) provided an argument, of which I
now sketch the outline, that motivates this conditional approach. Let ( Y,,, Y,,) denote the hth pair
of observations, where a "1" response denotes category 1 (success) and "0" denotes category 2.
Consider the logit model
+
pair, one eliminates the nuisance parameters { a h }
by conditioning on the pairwise success totals
{ ylh + Y , ~ }Given
.
a total of 0, P(Ylh = YZh= 0) =
1, and given a total of 2, P(Ylh = Y,, = 1) = 1.
The conditional distribution of ( Y, ,, Y2,) depends
on 0 only when y,, + YZh = 1. For each of the n*
such pairs, direct calculation using (2.2) shows that
P(Ylh = 1, Y,, = 0) = [l + exp(0)l-l. Since n,, =
Cy,, for these n* pairs, the distribution of n,,
conditional on n* is binomial with parameter [l +
exp(0)I-l. The parameter equals 112 under
marginal homogeneity ( 0 = 0). This conditional
analysis implies that pairs having identical response at the two occasions are irrelevant to testing
a , + =a+,.
Cox (1966) generalized this analysis for data in
which each case is matched with several controls.
Gart (1969) gave an exact test for comparing
matched proportions in crossover designs.
3. EXACT INFERENCE IN I x J TABLES
For I x J tables, statistical independence of X
and Y corresponds to the loglinear model M, =
(X, Y), which has form
log mij = p
+ AT + A;.
+
where the indicator ' I ( t = 2) equals 1 when t = 2
and 0 when t = 1. This model (a special case of the
Rasch model) permits separate response distributions for each pair, but assumes a common effect,
exp(0) representing the odds ratio of success at
occasion 2 compared with occasion 1. For the joint
mass function of the data under the assumption
that Ylh and Y,, are independent within each
To test this model against the saturated model
[ M, = (1.1) extended to I rows and J columnsl, one
uses the null distribution of inij} given { n , + }and
For multinomial sampling, the cell probability
parameters {aij} in the distribution of {n,,} can be
expressed in terms of the marginal probabilities
and (I - 1)(J - 1) odds ratios. Conditional on
i n , + } and {n+j}, Cornfield (1956) noted that the
distribution of inij} depends only on the odds ratios. The conditional probabilities are proportional
to
where a i j = (aijaIJ)/(aiJaIj).For exact tests of
statistical independence (all a i j = I), this distribution simplifies to the multiple hypergeometric. The
probability of a table inij} having the given
marginal totals equals
n!n
n n,,!
137
3.1 Linking Test Statistics to Alternatives
Let p,,, denote the null probability (3.1) of the
observed table. Freeman and Halton (1951) defined
the p-value for a conditional test of independence to
be the null probability of the set of tables having
probability no greater than pobs. For the 2 x 2
case, this simplifies to a two-sided version of
Fisher's exact test. Many statisticians have argued
that the p-value should instead be based on the
exact distribution of some meaningful statistic T
(such as the efficient score statistic) for quantifying
the departure of the data from Ho (e.g., Yates,
1934; Fisher, 1950; Healy, 1969; Agresti and Wackerly, 1977; Cressie and Read, 1989). The p-value
for the Freeman-Halton test may be regarded as
one that uses a statistic negatively related to p,,,,
such as T = - 2 log( p,,,).
When both classifications are nominal, the usual
alternative to the independence model is the saturated model. The efficient score statistic is then the
Pearson chi-squared statistic for testing the goodness-of-fit of the independence model,
where mij = ni+n . I n . One could let the p-value
be the null probabh!ity that X 2 is at least as large
as observed (Yates, 1934; Agresti and Wackerly,
1977; Baker, 1977). More generally, one could use a
power divergence statistic (Cressie and Read, 1989),
special cases of which are the Pearson statistic and
the likelihood-ratio statistic.
When both classifict$ions are ordinal, it is often
important to have good power for detecting a monotone trend in the association. To do this, one could
let T relate to a Spearman or Pearson-type correlation (Patefield, 1982), the difference between the
numbers of concordant and discordant pairs (Agresti
and Wackerly, 1977), or the Jonckheere-Terpstra
statistic (StatXact, 1991). In the first case, the
statistic on which the reference tables are ordered
is T = CCxiyj(nij - ni+n+j/n), .for two sets of
mon,otone scores { xi) for rows and { yj) for columns.
An exact conditional test using this statistic is an
efficient score test for the loglinear model of linearby-linear association (Agresti, Mehta and Patel,
1990). That model has the form
using T fall in a complete class of unbiased and
admissible tests.
When X is nominal and Y is ordinal, one might
let T be the Kruskal-Wallis statistic adjusted for
ties (Klotz and Teng, 1977). In that statistic, average ranks are used as scores {yj} for the column
categories, and the test is sensitive to variation
among the mean ranks computed for the conditional distributions within the rows. This is an
efficient score statistic for a loglinear model having
form (3.2), with "row effects" {fix,} treated as
parameters. For 2 x J tables, Soms (1985) presented exact tests sensitive to other alternatives.
See Haberman (1974), Goodman (1979) and Agresti
(1990, Chapter 8) for discussions of loglinear
models with scores.
To illustrate that tables that are "more contradictory" to Ho according to some statistic T need
not be less likely, consider the twelve 3 x 3 tables
having row totals (6,l, 2) and column totals (1,2,6).
Table 3 gives the conditional null distribution of
T = CCxiyjnij, for { x i = y, = i - 2). The distribution has support between - 7 and 0 with a mean of
- 2.22. It is-highly irregular, far from its limiting
normal distribution. Table 3 shows that, for some
margins, it is not possible to obtain small p-values
for some alternatives (e.g., a test using T for the
one-sided alternative of a positive association). The
table having entries by row (0,2,4/1,0,0/0,0,2),
one of two tables to have T = -2, has only a
quarter of the probability of the table having entries (1,2,3/0,0,1/0,0,2), the only table having
T = 0. Yet, the first table is less contradictory to
Ho than the second, using I T - E(T) I as the criterion. The first table has a p-value of 1.0 using this
criterion, but only 0.2856 in the Freeman-Halton
test. By contrast, the I x I table having nii = 1for
all i and nij = 0 for all i + j has P = (2/1!) using
) T - E(T) I but has P = 1.0 for the FreemanHalton test or the exact test using x2 as the
criterion.
TABLE3
Exact conditional distribution of T = CC(i - 2 ) ( j - 2)nij
under independence, for margins ( 6 , 1 , 2 )and ( 1 , 2 , 6 )
t
(Birch, 1965; Haberman, 1974; Goodman, 1979),
with the special case 0 = 0 representing statistical independence of X and Y. For model (3.2),
the sufficient statistics are T plus those for the
Cohen and
independence model ({ ni+),{ n,)).
Sackrowitz (1991) showed that nonrandomized tests
No.
of tables
Probabilities
P(T = t )
138 A. AGRESTI
When I = 2, ordering the tables by T is equivalent to ordering them by U = Cj yjnIj. Many statistical tests use U for various choices of scores
(Graubard and Korn, 1987). For arbitrary monotone scores, the exact test for T is a small-sample
version of a trend test proposed by Cochran (1954)
and Armitage (1955). The statistic with midrank
scores is used in exact Wilcoxon tests for ordered
categorical data (Klotz, 1966; Mehta, Pate1 and
Tsiatis, 1984).
3.2 Exact Estimation for I x J Tables
"Exact" confidence intervals are rarely used for
I x J tables, probably because of the complexity.
Reducing parameter dimensionality could be useful in many applications, by using an unsaturated
loglinear model. Agresti, Mehta and Pate1 (1990)
discussed confidence intervals for ordinal classifications when odds ratios satisfy the pattern
implied by the linear-by-linear association model
(3.2). For this pattern, the non-null distribution of
T = CCxiyjnij is
where C, is the sum of (11,IIjnij!)-l for all tables
with the given marginal distributions having T = t .
One obtains a confidence interval for 0 and thus
{aij} by inverting the test of H,:p = Po, as in the
case of the odds ratro for a 2 x 2 table. When T
takes its maximum or minimum possible value for
the given marginals, the conditional and unconditional ML estimators of P and asymptotic standard
errors do not exist, but one-sided confidence bounds
for 0 and odds ratios using it do exist.
4. EXACT INFERENCE IN THREE-WAY
CONTINGENCY TABLES
I,next consider three-way tables, paying special
attention to the 2 x 2 x K case. Such tables occur,
for instance, when one compares a binary response
(Y) for two treatments (X), using data obtained at
K levels of a possibly confounding factor (2).Two
hypotheses of importance are (1) conditional independence of X and Y, given Z, and (2) no threefactor interaction, meaning' that the true X-Y odds
ratio is identical at each level of Z. Conditional
independence of X and Y, given Z, corresponds to
the loglinear model symbolized by (XZ, YZ), and
no three-factor interaction corresponds to the loglinear model (XY, XZ, YZ). For cell-expected fre-
quencies { mijk),model (XY, XZ, YZ) has the form
log mi,,
=p
+ A?+ A+:
% + hEY+
+
and model (XZ, YZ) is its special case in which
0
xu {hij
- 1.
4.1 Testing Conditional Independence in 2 x 2 x K
Tables
A common approach to testing conditional independence [ M, = (XZ, YZ)] performs the analysis
under the assumption of no three-factor interaction
[MI = (XY, XZ, YZ)]. The sufficient statistics are
the X-Z and Y-Z two-way marginal tables for M,,
and these as well as the X-Y marginal table for
MI. The relevant conditional distribution is that of
the X-Y marginal table, given the X-Z and Y-Z
marginal tables. The conditioned totals are the
marginal counts for the K partial tables. For the
2 x 2 x K case, the distribution simplifies to that
of = Cknllk, given {nl+k,n2+k,n+lk, n+2k, =
1, . . . , K}. Under the assumption of no three-factor
interaction, Birch (1964) showed that UMPU tests
of conditional independence utilize T (see also
Lehmann 1986, pages 163-164).
Conditional on the strata margins, inllk, k =
1,. . . , K) have independent hypergeometric distributions, each of form (1.2) with 8 = 1. The product
of the K mass functions determines the null distribution of their sum. For the one-sided alternative
of a "positive" association (odds ratio greater than
1.0 in each level of Z), the p-value for Birch's exact
test of conditional independence is the null probability that Cknllk is at least as large as observed,
for the fixed marginal totals. Thomas (1975),
Pagano and Tritchler (1983b), and Mehta, Pate1
and Gray (1985) gave algorithms for implementing
this test, which may be regarded as an exact smallsample version of the Cochran-Mantel-Haenszel
test.
The exact McNemar test for matched pairs (Section 2.3) is a special case of Birch's exact test of
conditional independence. In this representation,
each of the n matched pairs has a 2 x 2 table
relating occasion (or member of pair) to response.
One tests M, = conditional independence of occasion and response, given the pair, under the assumption of MI = homogeneous odds ratios in the
n 2 x 2 tables.
For 2 x 2 x K tables in which it is unrealistic to
expect the K conditional X-Y odds ratios to be
similar or even of the same sign, the saturated
model is a more relevant alternative than no
three-factor interaction. In that case, the efficient
score statistic is CkX;, where X l denotes the
Pearson statistic for testing independence between
139
X and Y within the kth level of Z. This statistic is
commonly used in asymptotic tests of conditional
independence against the general alternative.
Currently, there do not seem to be any computer
algorithms for this case.
4.2 Testing Homogeneity of Odds Ratios in
2 x 2 x K Tables
Birch's exact test using test statistic C,n,,, assumes homogeneity of the odds ratios in the 2 x
2 x K table. Zelen (1971) presented an exact test of
this assumption. Here, M, = (XY, XZ, YZ), and
M, is the saturated model. The conditional distribution, given each of the three sets of two-way
marginal totals, has probabilities proportional to
(IIiIIjIIknijk!)-l.For the 2 x 2 x K case, the fixed
totals are in,,,, n,+,, n,,,, n,,,,
k = 1,. . K)
and {n,,,, n,,,, n,,,, n,,,). Zelen defined the pvalue to be the sum of probabilities of all 2 x 2 x K
tables that are no more probable than the observed
table. Alternatively, one could define P by ordering the tables with the given two-way margins
by x2 for testing the fit of loglinear model
(XY, XZ, YZ). Thomas (1975) and Pagano and
Tritcher (1983b) gave algorithms for implementing
Zelen's test.
To improve potential power in testing model
(XY, XZ, YZ), one could instead test it against an
unsaturated model. When the levels of Z have a
natural ordering, one could use the alternative loglinear model by which the log odds ratios change
linearly across the K strata; that is,
a
log mi,,= (XY, XZ, YZ)
+ I(i =j
=
,
1) 6zk,
for fixed monotone scores { z,), where I(.) is the
indicator function. The relevant distribution is then
that of ~ , z , n , , , , conditional on all two-way
marginal totals. Zelen (1971) also presented such
an exact test.
To illustrate some exact tests for conditional independence and for homogeneity of odds ratios for
2 x 2 x K tables, consider Table 4, a 2 x 2 x 3
table based on a larger table presented by Gast-
TABLE4
Example of 2 x 2 x K analysis
July
promotions
' August
promotions
September
promotions
Race
Yes
No
Yes
No
Yes
No
Black
White
0
4
7
16
0
7
4
13
0
2
8
13
Source: Gastwirth (1988).
wirth (1988, page 266). The data refers to P =
whether promoted and R = race, stratified by M =
month of promotion consideration (in 1974). Cases
involved GS-13 level computer specialists being
considered for promotion to level GS-14. (It appears
that many of the subjects appeared in two-or all
three strata, but this information is not available.
So, like Gastwirth, I treat the promotion decisions
as independent.) Under the assumption of a constant odds ratio 8 between P and R at each level of
M, we first test H,: 8 = 1 against H,: 8 < 1. In
using the one-sided alternative for the association
between P and R, the test is sensitive to evidence of
possible discrimination against blacks, in the sense
of the probability of promotions being lower for
blacks than whites. Given the P and R marginal
totals at each level of M, n,,, can range between 0
and 4, n,,, can range between 0 and 4, and n,,,
can range between 0 and 2. The test statistic T =
Cn,,, can assume values between 0 and 10, and
under H,, E(T) = 2.90 and u(T) = 1.35. Note that
the sample data represent the most extreme possible result in each of the three cases. The observed
test statistic is T = 0, and the p-value is the null
value of P(T r 0), which is 0.026. Zelen's test of
the hypothesis of a constant odds ratio is a test of
fit of the loglinear model (PR, PM, RM). The reference set consists of the subset of these 2 x 2 x 3
tables that also satisfy n,,+= 0. The observed table
is the only such table, so the test is degenerate,
giving P = 1.0.
4.3 Exact Estimation in 2
x 2 x K Tables
Assuming no three-factor interaction and conditioning on the strata totals, the joint distribution of
{ n . . . , n,,,)
is the product of K terms of the
type given in (1.2) for the 2 x 2 case. This distribution depends only on the common odds ratio. Birch
(1964) discussed the conditional ML estimator of
the common odds ratio, and Gart (1970) defined
"exact" confidence intervals as a direct extension
of Cornfield's "exact" intervals for 2 x 2 tables.
Thomas (1975), Pagano and Tritchler (1983b),
Mehta, Pate1 and Gray (1985), and Vollset, Hirji
and Elashoff (1991) provided algorithms for calculating such intervals. When the sufficient statistic
T = C knllk attains its minimum or maximum possible value, asymptotic confidence intervals (e.g.,
using the Mantel-Haenszel approach; see Agresti
1990, pages 235-236) do not exist, but one-sided
"exact" confidence intervals are well defined.
Table 4 has the boundary value T = 0, resulting
in a degenerate conditional ML estimate of 0.0 for
an assumed constant odds ratio. An "exact" 95%
upper confidence bound for the odds ratio equals
0.779, the value 8, that gives P = 0.05 in testing
,,,,
140 A. AGRESTI
H:, 19 = 6 , against HI: 19 < 19,. The median unbiased estimator equals 0.152.
4.4 Exact Methods for I x J x K Tables
In principle, methods for exact testing and esti,nation extend to loglinear models for multiway
tables. Current computational algorithms are restricted to certain analyses for 2 x J x K tables. In
this subsection, I outline the types of inferences for
which it would be useful to extend computational
algorithms in the I x J x K case.
Consider the hypotheses corresponding to the following five hierarchical loglinear models:
(1) (X, Y, 2): Mutual independence of X, Y,
and Z;
(2) ( X, YZ): Joint independence of X and the
Y-Z classification;
(3) ( XZ, YZ): Conditional independence of X
and Y, given Z;
(4) (XY, XZ, YZ): No three-factor interaction;
(5) ( XYZ): Saturated model.
For each of models (1)to (4), one can consider exact
testing against the alternative of the next most
complex model, and against the general alternative
of the saturated model.
Certain tests are special cases of ones already
developed for two-way tables, so they do not require
separate consideration. Hypothesis [2: (X, YZ)] is a
special case of statistical independence for a twoway table, in which the second classification consists of the J K combinations of categories of Y and
Z. Thus, an exact test of [2: (X, YZ)] against [5:
(XYZ)] is simply a standard exact test of independence for a two-way ( I x J K ) table. For instance,
in Table 4, an exact test of ( P , RM) (i.e., that
promotion is jointly independent of race and month
of decision) is an exact test for the two-way table
having rows (0,4,0,4,0,2/7,16,7,13,8,13). The
Pearson test statistic of 5.62 has an exact pvalue of 0.353. [The attained significance for
this joint test is weak compared to the p-value of
0.026 obtained for the P-R association alone in
the 'one-sided exact test of (PM, RM) against
(PR, PM, RM). This shows how severely the evidence represented by an effect of a certain size can
diminish when the degrees of freedom on which it
is based increase drastically.]
A test of [2: (X, YZ)] against [3: (XZ, YZ)l tests
whether the hxZ term in model (XZ, YZ) is zero.
By standard collapsibility results (e.g., Bishop,
1971; Agresti, 1990, pages 146 and 230), when this
model holds the hXZ term is identical to the hXZ
term in the saturated two-way loglinear model for
the marginal X-Z two-way table. Thus, one can
conduct an exact test of [2: (X, YZ)] against [3:
(XZ, YZ)] using an exact test of independence of X
and Z in that two-way table. For instance, one can
conduct an exact test of ( P , RM) against (PR, RM)
by simply testing whether P and R are independent in the marginal table (0,22/10,42); for this
table, the Pearson statistic has two-sided P =
0.056. Similarly, one can conduct an exact test of
[I: ( X, Y, Z)] against [2: ( X, YZ)] using an exact
test of independence of Y and Z in the two-way
Y-Z marginal table. Computational algorithms are
unavailable for the other situations, discussed in
the remainder of this subsection.
To test hypothesis [I: (X, Y, Z)l against the saturated model, one conditions on the sufficient statistics for (X, Y, Z), which are {n,++,n+j+,n++,).
The resulting mass function is
Stumpf and Steyn (1986) gave formulas for the
first- and second-order moments of the cell counts.
To construct an exact test of hypothesis [I:
( X , Y, Z)l against the saturated model, one could
use this distribution to generate the exact conditional distribution of the Pearson statistic for
testing the fit of model (X, Y, 2 ) . This case is
relatively unimportant, as the hypothesis of mutual independence is plausible in very few applications.
A more important case is a test of conditional
independence [3: ( XZ, YZ)] of X and Y against
[4: (XY, XZ, YZ)]. Such a test generalizes Birch's
test for 2 x 2 x K tables. Here, one tests conditional independence under the assumption that the
( I - 1 ) ( J - 1) odds ratios relating X and Y are
identical across the K levels of Z. Model (XZ, YZ)
has the sufficient statistics ({ni+k),{n+jk)), and
model (XY, XZ, YZ) has these plus { nij+). One
uses the distribution of ({ nij+} I { ni+k),
which is a product of multivariate hypergeometric
distributions from the various layers of Z. Let d
denote the ( I - 1)(J - 1) x 1 vector having elements
The efficient score test orders tables in the reference set using d'VP1d, where V is the null covariance matrix of d. Birch (1965) proposed an
asymptotic test of this type.
Alternatively, one could test conditional independence [3: (XZ, YZ)] against [5: (XYZ)], if one believes that the association between X and Y may
vary considerably across levels of Z. An efficient
score statistic is then the Pearson statistic for testing the fit of (XZ, YZ), which is C,X;, where X;
is the Pearson statistic for testing independence
between X and Y within the kth level of Z. This
test would also use the conditional distribution that
fixes the XZ and YZ marginal tables. Since it has
a broader alternative than the generalized Birch
test, this test would tend to be less powerful than
that test when model (XY, XZ, YZ) provides a decent approximation to the actual distribution.
An exact test of no three-factor interaction [4:
(XY, XZ, YZ)] for I x J x K tables generalizes Zelen's test for 2 x 2 x K tables. Here, the null hypothesis states that the (I - 1)(J - 1) odds ratios
relating X and Y are identical across the K levels
of Z. The relevant conditional distribution has
probabilities proportional to (IIiIIjIIknijk!)-l,for
the reference set of tables having XY, XZ and YZ
marginal tables identical to the observed ones. An
efficient score test against the general alternative
[5: (XYZ)] orders tables in the reference set by the
Pearson statistic for testing the fit of the model.
For ordinal variables, one would modify the above
ideas by constructing tests to increase power against
important alternatives, such as has been done for
I x J tables. For instance, to test conditional independence (XZ, YZ), one might choose an alternative model that implies a monotone conditional
X-Y association. One could use the single degreeof-freedom test statistic C ,{ C C xi yj[ nijk (ni+kn+jk)/n++kl)
to order the reference set of tables having the fixed X-Z and Y-Z marginal tables.
This results from comparing model (XZ, YZ) to a
model of homogeneous linear-by-linear association,
whereby the
term in model (XY, XZ, YZ) is
replaced by a term @xiyj for fixed monotone row
and column scores. Such a test would be an exact
analog of an asymptotic score test proposed by
Mantel (1963) and Birch (1965). .
There has been some work of this type for the
2 x J x K case with ordered levels of Y (Mehta,
Pate1 and Senchaudhuri, 1991). In this case, one
can take x, = 1and x, = 0, and consider the conditional distribution of C k[Cjyjnijk].For preselected
monotone scores, this gives a stratified version of
the Cochran-Armitage trend test. For rank { yj)
scores, it gives a stratified version of the Wilcoxon
test. Another application of this case is testing
marginal homogeneity in an I x I table with the
same ordered row and column categories. One can
conduct this test by conducting the exact test of
conditional independence for the 2 x I x n table,
141
where the two rows for stratum k contain one
observation in each row, giving the responses at
the two occasions for subject k. Such a test is an
exact analog of asymptotic tests described by White,
Landis and Cooper (1982) and Kuritz, Landis and
Koch (1988).
Interval estimation for I x J x K tables would
seem to be awkward except for simpler models that
reduce the dimensionality of the parameter space.
Examples include models that describe the X-Y
conditional association by a linear-by-linear term
or describe three-factor interaction by a linear trend
in conditional log odds ratios.
5. EXACT INFERENCE IN LOGISTIC
REGRESSION MODELS
Exact inferences for loglinear models discussed
in previous sections have counterparts for other
generalized linear models that use natural parameters as the basis of the link function. A closely
related example for categorical data is logistic regression modeling. Assuming a binomial distribution with parameter ?r for the response, one uses
the logit link, log[?r/(l - T)]. Logistic models are
particularly useful when highlighting one categorical variable in the contingency table as a response
and the others as explanatory. Loglinear models do
not make this distinction, although logistic models
with qualitative explanatory variables have equivalent loglinear representations.
For subject i, let yi denote a binary response,
and let x i = (xio,xi,, . . . , xi,) denote values of k
explanatory variables, where xi, = 1. The logistic
regression model is
Under the usual assumption that { yi) are independent Bernoulli outcomes, the sufficient statistic for
Pj is Tj = Ciyixij, j = 0,. . . , k. As noted by Cox
(1958b, 1970), one can conduct exact inference for
Pj using the distribution of Tj, conditional on {Ti,
i # j). Such inference is called conditional logistic
regression (Bayer and Cox, 1979; Breslow and Day,
1980, Chapter 7; Tritchler, 1984; Hirji, Mehta and
Patel, 1987).
To illustrate, Table 5 shows some data from a
case-control study (Shapiro et al., 1979) relating
cigarette smoking to myocardial infarction for
women of various ages using oral contraceptives.
142 A. AGRESTI
TABLE5 Example for exact logistic analysis Smoking level
(cigaretteslday)
Age
Disease status
0
1-24
>24
25-29 Myocardial infarction
Control
0
25
1
25
3
12
30-34 Myocardial infarction
Control
0
13
1
10
8
10
Source: Shapiro et al. (1979).
Let {nij,) denote the count at level i of smoking, j
of disease status, and k of age. Let {xi) denote
scores assigned to the levels of smoking. Let ?ri,
denote the probability of disease for subjects
at level i of smoking and k of age. One might
consider the model
Then {ni+,) are fixed for this model, and
are fixed by the retrospective nature of the study.
To conduct exact inference about p,, one considers
the distribution of C ,(C xi nil,) (the sufficient
statistic for PI), conditional on these totals. For
{x, = 0, x2 = 12.5, x, = 301, the conditional ML
is 0.130, and the exact p-value for
estimate of
testing p, = 0 against 0, > 0 is 0.000. Here, results are similar to those obtained with the unconditional ML analysis, for which the estimate of P,
of 0.133 has an estimated standard error of 0.042.
One could add an interaction term to the model and
do an exact analysis for it. This would be particularly natural with additional age strata representing greater variation in the age factor, since one
might expect the effect of smoking to increase at
higher age levels.
Several special cases of exact analyses for the
logistic model have received attention in the literature., For instance, Breslow and Day (1980), Peritz
(1982), and Hirji, Mehta and Pate1 (1988) discussed
exact inference for matched case-control studies
with the logistic model, in which case a subset of
the explanatory variables are used for matching.
When k = 1in (5.1), the exact test of 0, = 0 using
T, = C yi xi, given To = X i yi is a special case of
the linear-by-linear test described in Section 3.1
applied to I x 2 tables. Here, I represents the
number of distinct sample values of the explanatory variable, and the test may be regarded as an
exact version of the Cochran-Armitage trend test.
Difficulties can arise in exact inference for logistic regression when some explanatory variables are
@,
continuous. The { y,) values may be completely
determined by the given sufficient statistics, making the conditional distribution degenerate.
Algorithms for exact conditional inference for logistic regression can be applied to perform inference for equivalent loglinear models. To illustrate,
consider loglinear modeling of several I x 2 tables.
Regard the tables as a three-way I x 2 x K crossclassification of X, Y and 2. The loglinear model is
equivalent to a logistic model for response Y whenever that loglinear model has a general association
term relating X and 2. For instance, the logistic
model (5.2) for Table 5 corresponds to the loglinear
model having form
log mij,
= A,
+ A? + A? + A: + A?: + :A? + p,xiyj
where { y, = 1, y2 = 0) and S = smoking, D =
disease status, and A = age. This is a special case
of the loglinear model of homogeneous linearby-linear S-D association.
6. EXACT GOODNESS OF FIT
One can interpret tests of independence, conditional independence and no three-factor interaction
against general alternatives as tests of goodness of
fit of loglinear models. In principle, one could use
analogous methods to construct exact tests of goodness of fit for other loglinear or logistic regression
models. For a particular model M, the reference set
consists of all tables having the observed values for
the minimal sufficient statistics. Given that the
model holds, the conditional distribution of the data
given those sufficient statistics is independent of
any parameters. One could construct the test by
computing the null distribution of a goodness-of-fit
statistic, such as the Pearson statistic. The p-value
for testing the model is the conditional probability
'that the goodness-of-fit statistic is at least as large
as observed. The ML fitted values for the model are
the same for all tables in the conditional reference
set.
For instance, in testing independence with
Fisher's exact test, one also implicitly tests the
adequacy of the loglinear model of independence,
(X, Y). To test the more general model (3.2) of
linear-by-linear association in a two-way table, one
considers the conditional distribution of the data
given i n i + ) , {n+j),and CCxi yjnij.
McCullagh (1986) showed that, even for large
samples, it is beneficial to perform goodnessof-fit tests using the conditional rather than
unconditional distribution. Although an exact
goodness-of-fit test makes theoretical sense for any
model having simple sufficient statistics, a general
computer algorithm is not available for it. This is a
useful topic for future research. There is also a
need for work on exact distributions of localized
measures of goodness of fit, such as cell residuals.
Bedrick and Hill (1990) gave exact conditional tests
for a single outlier and for multiple outliers in
logistic regression.
7. COMPUTING FEASIBILITY
Doing computations for exact conditional inference requires working with the set of contingency
tables having the given values of the sufficient
statistics that are fixed for the inference. The potentially huge cardinality of the conditional reference set has been a severe impediment to the use of
exact tests.
To illustrate, a 4 x 4 table with only 20 observations can have as many as 40,176 tables with the
same margins; a 4 x 4 table with 100 observations
has a maximum cardinality on the order of 7.2 x
lo9. For given I and J and marginal proportions,
the number of tables in the reference set of I x J
tables with those fixed proportions increases exponentially in the sample size n. For fixed n, the
number of tables having given row and column
marginal proportions also increases rapidly as I
and J increase or as the row and column proportions become more homogeneous. For instance, a
5 x 5 table has a maximum cardinality on the
order of 2.1 x l o 6 for 20 observations and 9.2 x
1014 for 100 observations. Good (1976, 1977) and
Gail and Mantel (1977) gave approximations for
the cardinality, and Agresti and Wackerly (1977)
and Agresti, Wackerly and Boyett (1979) gave maxima for several table dimensions and sample sizes.
Enormous improvements achieved recently both
in algorithms and in computer power have made
exact inference much more feasible than it was a
decade ago. Most analyses conducted then with a
mainframe computer can be conducted now in the
same order of time on a personal computer. When
one is interested only in a p-value rather than the
entire distribution of some statistic, substantial
savings in time are obtained using algorithms that
do not require total enumeration of the reference
set (Pagano and Halvorsen, 1981; Mehta and Patel,
1983). With some algorithms, exact analyses can be
easily conducted on a PC running on MS-DOS when
the order of the cardinality is about lo7. They can
be conducted when the cardinality is much larger,
using computers having operating systems with
larger memory capacities.
To illustrate, consider the 3 x 4 table (60,4,1,
0/1,5,4,1/3,3,3,2), having n = 100 and 33,675 tables in the reference set. In 1978, I performed the
143
Freeman-Halton test using a state-of-the-art FORTRAN program on an IBM 3701165 in about 15
seconds CPU time. This year, I performed the same
analysis in 15 seconds total time using the software
package StatXact (1991) on a 386-version PC (the
CompuAdd 386SX, with math coprocessing chip)
running on MS-DOS at 16 mHz, and in 1second of
CPU time using SAS (PROC FREQ) on a workstation (DEC 3100). The 4 x 4 table (7,5,0,0/1,15,
1,0/0,7,7,0/0,0,4,9) has 12,798,781 tables in the
reference set. In 1977, Klotz and Teng (1977) estimated it would take 6 hours on a Univac 1110 at
the University of Wisconsin to perform an exact
Kruskal-Wallis test for a table having these margins. I performed this analysis with StatXact in
about 8 minutes of total time on a 386 version PC.
This table has a very small p-value (0.0000 rounded
to four decimal places), which results in considerable savings in time for algorithms that are able to
determine whether many tables contribute to the
p-value without explicitly enumerating all their
cells.
7.1 Algorithms
A variety of algorithms have been used in computing exact conditional distributions. Verbeek and
Kroonenberg (1985) presented a good survey of
those used for I x J contingency tables. These include algorithms that provide total enumeration of
the tables in the reference set (e.g., March, 1972;
Boulton, 1974; Baker, 1977; Cantor, 1979; Balmer,
1988), algorithms that compute the characteristic
function and invert it via Fourier transforms (e.g.,
Pagano and Tritchler, 1983a, b), network algorithms (Mehta and Patel, 1983) and Monte Carlo
algorithms (e.g., Agresti, Wackerly, and Boyett,
1979). Their paper also has enlightening discussions of practical problems related to the algorithms and hardware for implementing them, such
as how to ensure proper comparison of extremely
small probabilities.
Algorithms that provide total enumeration of the
reference set are very time-consuming, and adequate only for small problems. In the characteristic
function approach (Good, Gover and Mitchell, 1970;
Good, 1982; Pagano and Tritchler, 1983a, b), one
computes the characteristic function of the statistic
of interest (such as a goodness-of-fit statistic) using
a recurrence relation and then inverts it using a
Fourier transform to obtain the relevant distribution. The fast Fourier transform (Cooley and Tukey,
1965) is a popular method for fast convolution of
long sequences, and so it is a natural one to apply
to analyses (such as those for 2 x 2 x K tables)
involving convolutions of distributions. This method
is relatively space and time efficient, computation
144
A. AGRESTI
time increasing polynomially in the sample size,
rather than exponentially. However, Vollset, Hirji
and Elashoff (1991) noted that this method's use of
trigonometric functions and complex arithmetic can
introduce substantial round-off errors for non-null
calculations when there is a wide range between
the largest and smallest of the combinatorial coefficients that determine the exact distribution (e.g., a
ratio of the two exceeding about lo2').
Among the most popular and versatile programs
developed in the past decade have been ones using
the network algorithm. This algorithm has been
applied to several problems in a series of papers by
Cyrus Mehta, Nitin Pate1 and some coworkers. For
instance, see Mehta and Pate1 (1983, 1986) for its
application to Freeman-Halton exact tests for I x J
tables; Mehta, Pate1 and Tsiatis (1984) for exact
tests for 2 x J tables; Mehta, Pate1 and Gray (1985)
for inference for the common odds ratio in 2 x 2 x K
tables; Hirji, Mehta and Pate1 (1987) for exact logistic regression; Agresti, Mehta and Pate1 (1990)
for exact inference in I x J tables with ordered
categories; Mehta, Pate1 and Senchaudhuri (1991)
for inference for 2 x J x K tables; and Hilton,
Mehta and Pate1 (1991) for Smirnov tests for categorical or continuous data.
I provide here only a brief outline of the network
representation for the reference set for an I x J
table with fixed margins, and refer the reader to
the previously mentioned papers for technical details on the algorithm itself. The network representation consists of nodes and arcs, constructed in
J 1 stages. For k = 0,. . . , J, the nodes at stage
k have the form (k,w,), where w, = (w,, , . . . , w,,),
with wik = nil + .. + iiik and w, = 0. There are
as many nodes at stage k as there are possible
partial sums for the first k columns of the table.
Arcs emanate from each node at any stage k, each
arc being connected to a distinct node at stage
k 1. The network is constructed recursively by
specifying all successor nodes (k + 1,wk+,) that
are connected by arcs to each node (k, w,). A path
through the network is a sequence of arcs (0,O) -,
(1, w,) -, . + ( J , w,). Each path represents a
distinct table in the reference set, with entries
(w,+, - w,) in column k + 1. The network representation is used in calculating the exact distribution by stagewise recursion, beginning at node
(0,O). Figure 1 shows the network representation for the 3 x 3 table discussed in Section 3.1
having row margins (6,1,2) and column margins
(1, 2, 6). The topmost path gives the table
3/0,0,1/0,0,2).
For simply calculating p-values, one can dramatically increase the speed of the network algorithm
by computing at each node lower and upper bounds
on test statistic values for tables having path passing through that node. In this way, one can determine tables that necessarily do or do not contribute
to the p-value, without processing the remaining
parts of paths in the network passing through that
node. Vollset, Hirji and Elashoff (1991) gave an
adaptation of the network algorithm that uses an
algebraic rather than geometric representation,
treating convolutions of distributions using polynomial multiplication. Their algorithm computes the
null distribution on the natural rather than logarithmic scale. Using this approach, they obtained
faster computation of "exact" confidence limits for
the common odds ratio in 2 x 2 x K tables.
These days, algorithms such as the network algorithm can practically handle analyses for most I x
J and 2 x 2 x K tables having a small to moderate
number of cells and small to moderate cell counts.
To illustrate, Table 6 reports the CPU time on a
+
+
FIG. 1. Network representation for 3 x 3 tables having row
margin ( 6 , 1 , 2 )and column margin ( 1 , 2 , 6 ) .
TABLE
6
Sample CPU times (seconds) for Freeman-Halton test on
I x I tables with uniform marginal counts andp-values
approximately 0.05, using SAS on a DEC 3100 workstation
Total sample size
a More
than 6 hours.
DEC 3100 workstation needed for SAS (PROC
FREQ) to perform the Freeman-Halton test for
I x I tables of various sizes having uniform
marginal totals and cell frequencies changed from
uniformity sufficiently to give p-values approximately equal to 0.05. Times are much faster when
marginal counts are nonuniform. For instance, a
5 x 5 table with n = 100 having both margins
equal to (60,25,10,3,2) took only 40 seconds CPU
time.
The remaining problematic area is large, sparse
tables, for which there are a large number of cells
and cell counts too small to appeal to standard
asymptotic theory. For two-way tables, examples
would be tables of at least about 50 cells, having
several fitted values less than about 5. Even with
current computing power, the conditional reference
set for such tables is often too large to be handled
by exact methods. Moreover, the cardinality grows
so rapidly as a function of the number of cells that,
regardless of future improvements in computer
power, it may always be possible to produce tables
that cannot be handled exactly.
A good compromise for handling large, sparse
tables is to estimate precisely the inferential characteristics of interest, such as exact p-values and
confidence intervals. One can do this using Monte
Carlo sampling of tables in the reference set, by
simulating the conditional hypergeometric Sampling distribution (Agresti, Wackerly and Boyett,
1979; Boyett, 1979; Cox and Plackett, 1980; Patefield, 1981, 1982; Kreiner, 1987; StatXact, 1991).
Each sampled table provides a Bernoulli random
variable, indicating whether the test statistic is at
least as large as observed. The estimated exact
p-value is the sample mean of those Bernoulli random variables, which is the proportion of sampled
tables that have test statistics at least as large as
the observed one. The precision of the estimate
is determined by the estimated sample variance
P ( l - P)/N, where N is the number of tables Sampled. Sampling of 17,000 tables ensures the estimate is good to within 0.01 with confidence at least
0.99.
Agresti (1990, page 308) gave a 16 x 5 table with
219 observations, relating various characteristics of
alligators to their primary food choice (having categories: fish, invertebrate, reptile, bird, other). For
such large tables, one could either sample a fixed
number of tables N to guarantee a certain accuracy, or repeatedly take samples of N' tables until
achieving a certain accuracy. Using the latter approach with N' = 2,000 and desired accuracy
0.0005 for a p-value for testing independence with
the Pearson statistic, Monte Carlo sampling of
10,000 tables provides an estimated exact p-value
145
of 0.0004 and a 99% confidence interval for that
exact p-value of (0.0000,O.0009).
An advantage of the Monte Carlo method is that
the amount of computational work is much less
dependent on the sample size n and table size I x J
than for methods for exact analysis. For a method
of simulating tables that involves taking a random
permutation of n integers, Agresti, Wackerly and
Boyett (1979) noted that the CPU time is approximately linear in n and stable in I and J. Patefield
(1981) provided a method that is more efficient for
large n. Although it takes longer to generate each
table with Monte Carlo methods, only a relatively
small number need to be generated. In principle,
this method could be used to approximate precisely
any exact analysis, including those for which exact
calculations may never be feasible.
Mehta, Pate1 and Senchaudhuri (1988) described
a more sophisticated and faster Monte Carlo approach, using importance sampling. Tables are
sampled from the conditional reference set in proportion to their importance for reducing the variance of the estimated p-value, rather than in
proportions corresponding to their hypergeometric
probabilities. In importance sampling, each Sampled table provides an estimate of the p-value that
is designed to be much better than the crude
Bernoulli estimate provided by Monte Carlo Sampling. The tables are sampled using a network
algorithm. For linear rank tests for 2 x J tables,
they noted that importance sampling can be up to
four orders of magnitude more efficient than Monte
Carlo sampling. That is, to achieve a certain fixed
accuracy with a p-value estimate, the ratio of the
number of tables sampled using the Monte Carlo
approach versus importance sampling was about
10,000 for tests such as the trend test. However,
the initial overhead involved in using importance
sampling, due partly to using backward induction
with the network algorithm to set up the networkbased sampling scheme, makes it inefficient for
certain very large data sets.
' 7.2 Software
Until recently, software for exact methods for
contingency tables was nearly nonexistent, at least
in the most popular statistical packages. Even now,
nearly all packages can perform Fisher's exact test
but little if anything else. With a couple of exceptions, our discussion here is limited to the most
commonly used packages.
SAS (using procedure FREQ) and IMSL (using
routine CTPRB) can perform Fisher's exact test
and the Freeman-Halton extension for I x J tables, but do not give options to perform tests that
base the p-values on goodness-of-fit statistics or
146
A. AGRESTI
ordinal statistics. SAS uses the network algorithm
from Mehta and Pate1 (1983), whereas IMSL uses
an algorithm that enumerates the entire reference
set. Thus, although neither program seems to have
limits on table sizes, SAS can handle a much greater
variety of tables in a reasonable amount of time.
Currently, BMDP and SPSSXonly perform Fisher's
exact test. All these packages seem to use the table
probability as the basis of ordering the reference
set for two-sided p-values.
StatXact (1991) is a statistical package specializing in exact nonparametric inference and in exact
inference for contingency table problems. Developed by Mehta and Pate1 and colleagues, it uses
versions of the network algorithm described in their
articles. For 2 x J tables, StatXact performs a general linear rank test that includes as special cases
the Wilcoxon test and a trend test with arbitrary
scores. For I x J tables with min(I, J ) I 5, it can
perform the Freeman-Halton test and exact tests
of independence using the Pearson or likelihoodratio chi-squared statistics. For tables with ordered
columns, it can perform the exact test using the
Kruskal-Wallis statistic when max(I, J ) I 5.
When rows are also ordered, it can perform the
exact test of linear-by-linear association described
by Agresti, Mehta and Pate1 (1990) when min(I, J )
s 5, and it can use the Jonckheere-Terpstra statistic when I s 5. For 2 x 2 x K tables, it performs
Birch's exact test of conditional independence (for
K I 200), Zelen's exact test of homogeneity of odds
ratios, and "exact" confidence intervals for an assumed common odds ratio. For 2 x J x K tables,
StatXact performs str-atified linear rank tests and
can perform inference for parameters in conditional
logistic regression models. For I x J and 2 x J x K
tables, StatXact performs Monte Carlo sampling
for cases beyond its capability for exact inference
(as long as I s 50, J s 50, K 5 200, and I J K s
2500), and it can perform importance sampling for
2 x J tables. The StatXact manual is also a good
reference for many examples of exact conditional
analyses. Many of the StatXact routines are
also available in the EGRET statistical software
package (EGRET, 1991).
Baptista and Pike (1977) gave a FORTRAN program for the Sterne-type confidence interval for the
odds ratio in a single 2 x 2 table. For 2 x 2 x K
tables, Thomas (1975) gave a FORTRAN program
for the conditional ML estimate and an "exact"
confidence interval for a common odds ratio, and
for exact tests of conditional independence and
no three-factor interaction (when K I 20). This
program can be slow since, unlike algorithms
presented by Pagano and Tritchler (1983b) and
StatXact (1991), it requires evaluating every table
in the conditional reference set. Vollset and Hirji
(1991) gave a fast GAUSS program for the exact
test of conditional independence and confidence interval for a common odds ratio, and indicated that
it can handle up to about 1,000 points in the distribution of C , n,,, .
8. OTHER APPROACHES TO EXACT
INFERENCE
This discussion has focused on the classical conditional approach to exact inference for contingency
tables. This section discusses controversies regarding that approach and describes alternative
approaches that produce results having some connection with those for exact conditional methods.
8.1 Controversy Over Exact Conditional Approaches
Most of the debate about exact conditional methods for categorical data has focused on their use
with Fisher's exact test when both margins of the
table are not naturally fixed. I discuss the controversy only briefly, as it has already generated an
enormous literature. See, for instance, Barnard
(1945, 1947, 1949, 1979, 1989), Berkson (1978),
Basu (1979), Kempthorne (1979), Upton (1982),
Suissa and Shuster (1984, 1985), Yates and discussants (1984), Bhapkar (1986), Haber (1987, 1989),
D'Agostino, Chase and Belanger (1988), Lloyd
(1988b), Rice (1988), Little (1989), Camilli (1990),
Mehta and Hilton (1990), Routledge (1990), Storer
and Kim (1990), and Greenland (1991).
The perceived problem with the test results
mainly from the conditional distribution of n,, (or
the odds ratio) being highly discrete, much more so
than when one or neither margin is fixed. This
results in the test being quite conservative in a
conditional or unconditional sense, when used with
a fixed significance level such as a = 0.05. The
actual probability of rejecting the null hypothesis
may be considerably less than the nominal level.
Proponents of Fisher's test (e.g., Yates, 1984) argue that (1) one should not use arbitrary fixed
significance levels (versus simply reporting the
p-value), (2) one should not average into the calcu lation of the p-value other tables whose marg i n a l ~did not occur, and (3) no substantive loss of
information about H,, results from conditioning
on the marginals (i.e., the marginal counts are
approximately ancillary).
The randomized-decision version of Fisher's exact
test, using randomization on the boundary of a
critical region to achieve a fixed significance level
a , is UMPU (Tocher, 1950). This is of little solace
for practical work, since randomization is not used,
although it indicates that Fisher's test may also
147
perform well for the mid-p definition of a p-value.
The mid-p value is half the probability of the observed result plus the probability of more extreme
values. For discrete data, the mid-p value has null
behavior more nearly like a uniform ( 0 , l ) random
variable than the ordinary p-value (e.g., its null
expected value is 0.5, and the sum of its two onetailed p-values equals 1.0). It has been recommended (e.g., by Lancaster, 1961; Plackett in the
discussion of Yates, 1984; Barnard, 1989, 1990) as
a good compromise between having a conservative
test and using randomization on the boundary to
eliminate problems from discreteness. For comparing two binomial probabilities n1 and a,, Hirji,
Tan and Elashoff (1991) noted that the mid-p adjustment to Fisher's exact test has actual levels of
significance closer to nominal levels than do classical asymptotic tests. This is especially true when
the common value of i n i ) is near 0 or 1or when the
sample sizes nl+ and n,, are quite different. Haber
(1986b) obtained similar conclusions both for comparing binomials and for multinomial sampling
over the four cells. Hirji (1991) showed that, for
tests of parameters in conditional logistic models
for case-control designs with unmatched binary
covariates, mid-p adjustments to an exact test perform well in approximating nominal levels compared to the exact test and asymptotic score tests.
Analogous remarks apply to "exact" interval estimation. Although necessarily conservative, "exact" interval estimates for the odds ratio can be so
highly conservative as to be less useful than
asymptotic large-sample approaches in terms of
long-run coverage performance. An adaptation of
Cornfield's "exact" method uses a mid-p adjustment, choosing 0 endpoints that have one-sided
mid-p values of a / 2 . This approach does not guarantee the desired coverage probability, but simulations by Mehta and Walsh (1992) showed that it
performs well in this respect. For small samples,
the mid-p-adjusted intervals can be much narrower.
For instance, for Fisher's tea-tasting data, having
,rows (3,1/1,3), the "exact" 95%' confidence interval' is (0.21,626.2), and the mid-p-based 95% confidence interval is (0.31,308.6). Vollset, Hirji and
Afifi (1991) showed similar good performance of the
mid-p approach for interval estimation of parameters in conditional logistic designs.
8.2 Exact Unconditional Approach
Other methods for 2 x 2'tables have been motivated by the controversy about the conditioning
argument and the conservativeness of Fisher's exact test. Some authors argue that it is better to use
a robust asymptotic test than a possibly highly
conservative exact test (e.g., D'Agostino, Chase
and Belanger, 1988). Others recommend using an
"exact" unconditional test.
To illustrate the latter approach, consider testing
n, = n, (0 = 1)for two independent binomial samples. One first computes an exact p-value P ( a ) for
each possible common value n of n, and n,. For
this p-value, one might use the product binomial
probability that a z statistic (or chi-squared statistic) for comparing two proportions is at least as
large as observed, when n is the value of the
nuisance parameter. The global p-value for the test
with unknown n is then P = sup, P(n). Proposed
by Barnard (1945, 1947), this test was disavowed
by him in later publications (e.g., Barnard, 1949,
discussion of Yates, 1984). Boschloo (1970), McDonald, Davis and Milliken (1977), Suissa and Shuster
(1984, 1985), Haber (1986a, 1987, 1989), Shuster
(1988) discussed computational implementation of
the unconditional test. Suissa and Shuster (1991)
extended it to comparisons of dependent proportions for matched pairs.
The 2 x 2 table having entries (3,0/0,3), discussed by Barnard (1945) and Fisher (1945) [see
also Little (1989) and Routledge (1990)1, illustrates
that results with the conditional and unconditional
approaches can be quite discre ant. For HI: 0 # 1,
= 0.100, and the
the Fisher p-value is 2(:)(:)
asymptotic Pearson chi-squared test has a p-value
of 0.014. For the exact binomial test of n, = a,
having only fixed row totals (3,3), the Pearson
chi-squared value of 6.0 that occurs for the observed table and for table (0,3/3,0) is the maximum possible, and the p-value for given nuisance
parameter a is 2a3(1 - a)3. The supremum of this
over 0 Ia I1occurs at n = 112, giving p-value =
1/32. This unconditional p-value is related to
Fisher conditional p-values by
fi:)
6
P ( X 2 2 6)
=
p(x22 6 1 n,,
= k) P ( n + , = k ) .
k=O But the conditional probability that X 2 2 6 is zero
except when n,, = 3, so the unconditional p-value
is a weighted average of the Fisher p-value for the
observed column marginals and p-values of 0 corresponding to the impossibility of getting results as
extreme as observed if other marginals had occured, that is, 1/32 = 0.10[(:)(1/2)'], where the
term in brackets is the binomial probability (when
a = 112) of the column marginals (3,3). Fisher
(1945) remarked, "It is my view that the existence
of these less informative possibilities should not
affect our judgment of significance based on the
series actually observed. . . . The fact that such an
unhelpful outcome as these might occur, or must
148
A. AGRESTI
occur with a certain probability, is surely no reason
for enhancing our judgment of significance in cases
where it has not occurred;. . . it is only the sampling distribution of samples of the same type that
can supply a rational test of significance."
The unconditional test is so computationally intensive that it seems complex to extend it to larger
contingency-table problems (Mehta and Hilton,
1990). In addition, removing nuisance parameters
by taking a supremum may itself produce quite
conservative analyses for tables having larger
numbers of such parameters. It should be noted
that asymptotic procedures can also be quite conservative. For instance, Koehler and Larntz (1980)
noted that the likelihood-ratio test tends to be
highly conservative when most expected frequencies are smaller than 0.5. To illustrate, consider
the 3 x 9 table (0, 7, 0, 0, 0, 0, 0, 1, 111, 1, 1, 1,
1,1,1,0,0/0,8,0,0,0,0,0,0,0),
discussed in the
StatXact manual. For the likelihood-ratio statistic,
the asymptotic p-value is 0.0837 and the exact
p-value is 0.0015; for the Pearson statistic, the
values are 0.1342 and 0.0013.
8.3 Bayesian Approaches
For 2 x 2 tables, Bayesian approaches using certain "conservative" prior distributions give results
equivalent to conditional tests. For instance, Altham (1969) gave an exact Bayesian analysis comparing parameters for two independent binomial
samples. She tested H,: a, 5 a, against a, > a,
using a beta(a,, Pi) prior distribution for a,; i.e.,
the prior for a, is proportional to (?ri)OL(l - a,)@,
with a = a, - 1 and,@= Pi - 1, i = 1,2. The posterior distributigns are beta(a:, (3:) with a: = a i
nil and Pi = Pi + ni2.Taking the Bayesian p-value
to be the posterior probability that a, 4 ?r, Altham showed this equals the one-sided p-value for
Fisher's exact test when one uses improper prior
distributions (a,, (3,) = (1,O) and (a,, (3), = (0,l).
This represents prior belief favoring the null hypothesis, in effect penalizing oneself against concluding that a, > ?r,. If a, = =,y, i = 1,2, where
0 +y I1, Altham showed that the Bayesian pvalue is smaller than the Fisher p-value, and the
difference between the two is no greater than the
null probability of the observed data.
Altham (1971) also gave Bayesian analyses for
dependent proportions. For a simple model in which
the probability of success is the same for each
subject at a given occasion, 'she again showed that
the classical p-value is a Bayesian p-value for a
prior distribution favoring the null hypothesis. For
a model similar to Cox's, in which the probability
of success varies by subject but the occasion effect
+
@,
is constant, she showed that the Bayesian evidence
against the null is weaker as the number of pairs
giving the same response at both occasions increases, for fixed n,, and n,,. This result differs,
and is perhaps more intuitively pleasing, than the
exact conditional ML result. For examples of other
Bayesian analyses for 2 x 2 tables, see Leonard
(1975), Chen and Novick (1984) and Nurminen and
Mutanen (1987).
9. FUTURE RESEARCH
By the turn of the century, we should see advances in applicability of exact methodology for
contingency tables at least comparable to those of
the past decade. One does not need a crystal ball to
predict that computer speed will continue to increase and algorithms will be further improved, so
that tables not now feasible for analysis soon will
be. In addition, it is reasonable to expect development of algorithms to handle new types of categorical data, in particular, more complex relationships
for larger tables in higher dimensions.
This article has emphasized exact inference for
contingency tables in the context of loglinear modeling. I believe that presenting the methods in the
context of inference for parameters in models helps
to unify a variety of exact conditional methods. It
also helps to identify areas in which additional
research would yield fruitful results, such as exact
inferences for I x J x K tables and a general goodness-of-fit test for loglinear models. This section
summarizes other avenues for future research.
An important but difficult area for future research is exact analysis of model parameters for
contingency tables that are large and sparse. For
such data, standard asymptotic methods behave
poorly; yet, it has been impractical to apply exact
methods. The size of the conditional reference set is
often too large to handle when the table has a large
number of cells. This can also happen when the
table is small but contains very large as well as
small cell counts. For such problematic tables, there
should be additional research on (1) hybrid algorithms that use exact methods for some parts of the
computation and approximations for other parts
(Baglivo, Olivier and Pagano, 1988), (2) fast ways
of simulating the exact distribution (Kreiner, 1987;
Mehta, Patel and Senchaudhuri, 1988), and (3) better asymptotic approximations (e.g., Koehler, 1986),
including saddlepoint approximations (Davison,
1988; Booth and Butler, 1990; Bedrick and Hill,
1992; Pierce and Peters, 1992).
An area in which large, sparse tables commonly
occur is modeling of longitudinal data with categorical responses. Conditioning on sufficient statistics
to eliminate nuisance parameters, such as subject
random effects, can be very helpful. Even then,
the large number of cells in the table make exact
methods or approximations for them quite challenging. Another complication is that some useful
models do not have reductions of data through
sufficiency, such as loglinear and logistic models
for the marginal probabilities rather than joint cell
probabilities.
An area that has scope for lots of additional work
is exact inference for I x J x K tables. Section 4.4
described several exact inferences for standard loglinear models. Other exact inferences are of interest when at least one variable is ordinal. In testing
conditional independence, one could use test statistics designed to improve power for narrower alternatives. For instance, Section 4.4 discussed the
possibility of testing conditional independence
against a linear-by-linear alternative. When X is
nominal and Y is ordinal, one could use a statistic
analogous to a stratified Kruskal-Wallis statistic.
Such a statistic results from comparing (XZ, YZ) to
a loglinear model in which the X-Y partial association term has the form p i yj, for parametric row
effects { p i ) and rank scores { y j ) . Efficient score
statistics for these cases are equivalent to statistics
proposed by Birch (1965) and Landis, Heyman and
Koch (1978) for large-sample testing of conditional
independence. Similar remarks apply to exact tests
of no three-factor interaction. Greater power for
narrower alternatives (unsaturated models for
three-factor interaction) would be achieved by
using statistics that treat one, two or all three
variables as ordinal. Of course, exact inferences for standard loglinear
models and for models recognizing ordinality are
also relevant for four-way and higher dimensional
tables. Many tests of mutual independence or conditional independence can be re-expressed in terms
of two-way or three-way tables, but not all. Also,
complications may result in tests about three-factor
and higher order interactions. The reference set of
'tables having the required fixed 'marginals may be
difficult to generate or simulate. For instance, in a
four-way table, an exact test of no three-factor interaction deals with a reference set in which all six
two-way margins are fixed.
There is unlikely to be as much controversy with
exact conditional methods for models for higher
dimensional tables as there has been with Fisher's
exact test for 2 x 2 tables. That controversy is
probably due less to philosophical disagreements
about conditioning than to practical consequences
of using. continuously defined measures such as
p-values with highly discrete distributions. Except
149
in extreme cases in which nearly all observations
fall in one or two marginal categories for each
classification, sampling distributions are much
"less discrete" for larger tables. As the number of
dimensions or the number of response categories
per dimension increases, the sampling distributions
that determine exact tests and confidence intervals rapidly approach a continuous form. Thus, the
conservativeness of the inferences becomes less
problematic. For instance, performance of exact
conditional tests should be closer to that of their
randomized versions, many of which are UMPU.
Also, the three approaches discussed for confidence
intervals for odds ratios (test-based with a / 2 in
each tail, adapted Sterne, test-based with mid-p
values) should all have true confidence levels close
to the nominal one.
For cases in which discreteness is problematic,
there should be more work on developing adaptations of exact methods that, while perhaps sacrificing "exactness," give performance closer to the
randomized exact versions. Examples are adaptations of exact tests and confidence intervals using
the mid-p value (e.g., Hirji, Tan and Elashoff, 1991;
Mehta and Walsh, 1992), and tests and confidence
intervals that use the exact distribution computed
at the ML estimate of the nuisance parameter (e.g.,
Conlon and Thomas, 1990; Storer and Kim, 1990).
So far there has been relatively little attention
paid to algorithms for calculating power for exact
conditional tests. The literature refers almost exclusively to 2 x 2 tables. For instance, Gail and
Gart (1973), Haseman (1978) and Suissa and
Shuster (1985) discussed the determination of sample size for obtaining desired powers in fixed-a use
of Fisher's exact test.
Finally, it would be very useful to have a general-purpose algorithm for exact tests comparing
two nested loglinear models. This would unify exact tests of goodness of fit for all loglinear models,
as well as tests against unsaturated alternatives.
In summary, much has been accomplished in the
past decade, but this is only the tip of the iceberg
of what can be done. In the future, statistical practice for categorical data should place more emphasis on "exact" methods and less emphasis on
methods using possibly inadequate large-sample
approximations.
ACKNOWLEDGMENTS
This work was partially supported by NIH Grant
GM43824. The author appreciates helpful comments on manuscript organization and content from
Dr. Cyrus Mehta and two referees. Computations
150
A. AGRESTI
for many of the examples presented in this article
were obtained using StatXact (1991).
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Comment
Edward J. Bedrick and Joe R. Hill
We congratulate
Professor Agresti
for his comprehensive review of exact inference with categorical data. We share his enthusiasm for exact
conditional methods and believe that the coming
years will produce many important computational
breakthroughs in this area.
The mechanics of conditioning on sufficient
statistics to generate reference distributions for estimation, testing and model checking with loglinear models for Poisson data and logistic regression
models for binomial data are well-known, but the
utility of conditioning in these settings is not universally agreed upon. Furthermore, the role of conditioning- in the analysis of discrete generalized
linear models with noncanonical link functions has
received little attention from most of the statistical
community. As a result, scientists and statisticians
are familiar with conditional methods, but many
are unsure how ,such methods should be incorporated into a n overall strategy for analyzing categorical data. We feel that the use and abuse of
conditional methods will not be fully understood or
appreciated without such a strategy. We hope that
Professor Agresti's survey and the ensuing discussions stimulate further work in this direction.
Edward J. Bedrick is an Assistant Professor, Department of Mathematics and Statistics, University
of New Mexico, Albuquerque, New Mexico 87131.
Joe R. Hill is an R&D Specialist at EDS Research,
5951 Jefferson Street, NE, Albuquerque, New
Mexico 87109.
CHECKING LOGISTIC REGRESSION MODELS
We would like to convey some of our recent work
on model checking for logistic regression and some
of our thoughts regarding conditional inference.
For the sake of simplicity, we assume that a single
model is under consideration. A little notation is
required. The usual logistic regression model has
two distinct parts: a sampling component and a
structural component. The sampling component
specifies that Y = (Y,, . . . , Y,)' is a vector of independent binomial random variables with Y,
Bin(m,, n,). The structural or regression component of the model is given by
-
logit ( n )
=
XP,
where logit(*) is a n n x 1 vector of log-odds with
elements log{ a,/ ( l - n,)), X is a n n x p fullcolumn rank design matrix with ith row xi, and 0
is a p x 1 vector of unknown regression parameters. Under model (I), S = X' Y is sufficient for 0.
Let 7i be the MLE of a under this model.
The distribution of the data pr(Y; P), indexed by
0, can be factored into the marginal distribution of the sufficient statistic S, and the conditional distribution of the data given the sufficient
statistic:
Taking a Fisherian point of view (Fisher, 1950),
inferences about 0 are based on pr(S; P), whereas
model checks use the conditional distribution
pr(Y 1 S). Letting sobs= X' yobs be the observed
value of the sufficient statistic for the logistic model,

A Survey of Exact Inference for Contingency Tables Alan Agresti Statistical Science

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